(STL ) Sparked by the breakthrough of Alphafold 1 [1]} and Alphafold 2[2]} in protein structure prediction, deep learning has been trusted in molecule interaction distance map prediction to learn structure information. The inclusion of CPI-structure, i.e. protein function prediction portal calibration is inspired by recent success in protein structure prediction led by the great work of AlphaFold1[1]} and AlphaFold2[2]}. Specifically, we pretrain the model to predict residue-residue contacts for a protein whose structure is solved and chemical atom-protein residue contacts given a known CPI complex structure. There are three popular ways of residue-residue pairwise distance matrix prediction depending on how to formulate it as a machine learning task. On the one end is to formulate it as a binary classification where a distance threshold is set defining whether a pair of residues are in contact or not, hence the name contact prediction. On the other end is to formulate it as a regression problem where the exact distance is used as a regression target, hence the name exact distance prediction. AlphaFold1 showed another way in between the two ends, which is to formulate it as a multi-class classification problem, where the distribution of pair-wise residue distances is broken down into multiple class labels according to a histogram, hence the name distogram prediction. We first focus on residue-atom pair wise distance at binding sites and then experiments contact prediction and distogram prediction. In our results, the two formulations have similar performance in light of the final CPI prediction through ablation study as shown in Supplemental Figure SREF .
